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Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing

机译：基于共识的集团任务分配，在空间众包中的社会影响

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摘要

With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely.
机译：通过支持GPS的智能设备和增加的无线通信技术，空间众包（SC）在为移动工人分配位置敏感的任务时，空间众包（SC）越来越受到关注。在真实世界场景中，对于复杂的任务，SC更有可能将每个任务分配给多个工人，称为组任务分配（GTA），因为单个工作者无法通过自己完成任务。分配工人组是一个有挑战性的问题，他们对他们感兴趣并愿意表现的任务。在本文中，我们提出了一种基于Worker Groups的首选项的组任务分配的新颖框架，其中包括两个组件：基于社交影响的偏好建模（SIPM）和偏好感知组任务分配（PGTA）。 SIPM采用二分图形嵌入模型和注意机制，以了解不同的工作组对不同工人组的基于社会影响的偏好。 PGTA利用基于树分解技术的最佳任务分配算法来最大限度地提高整个任务分配，其中我们为工人组提供更高的优先级，显示在任务中更具兴趣。我们通过提出提高组任务分配的有效性的策略进一步优化原始框架，其中考虑了深度学习方法和集团共识。广泛的实证研究验证了所提出的技术和优化策略可以很好地解决问题。

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